MMTV-Wnt-1转基因小鼠作为高发乳腺癌动物模型的观察

目的　观察MMTV Wnt 1转基因小鼠的乳腺癌发病情况及病理学变化规律。方法　观察MMTV Wnt 1转基因小鼠肿瘤发生情况 ,并采用原位移植将瘤组织置于裸鼠皮下 ,通过组织病理学切片来观察MMTV Wnt 1阳性转基因小鼠和移植鼠的病理学变化。结果　MMTV Wnt 1转基因小鼠最早从 7周龄开始出现乳腺瘤 ,发瘤鼠剖检可见脾、肝有不同程度的肿大 ,其他器官无明显病变 ;病理组织学检查发现发瘤鼠各脏器有不同程度的病变 ,但未出现肿瘤转移。将瘤组织移植裸鼠后 ,肿瘤可在裸鼠皮下生长 ,移植肿瘤病理学形态与原发瘤一致 ,未出现转移。结论　实验结果验证MMTV Wnt 1转基因小鼠可稳定自发乳腺肿瘤 ,可作为研究乳腺癌的良好的动物模型     

Wistar大鼠自发性乳腺肿瘤细胞系的建立及其生物学特性

目的 从Wistar大鼠自发性乳腺肿瘤中分离出乳腺肿瘤细胞系(ZHL2006),并对其进行鉴定,为动物模型的开发、肿瘤发病机制的探讨提供有价值的线索.方法 对一例Wistar大鼠自发性乳腺腺癌瘤组织进行培养.对传代培养细胞进行细胞生长曲线测定、细胞形态结构分析、细胞染色体分析,检测广谱细胞角蛋白(PCK)表达、软琼脂集落形成实验等检测.将细胞接种于10只裸鼠,并对形成的肿瘤进行病理组织学检查,并进行分离培养.结果 ZHL2006细胞在体外生长迅速,群体倍增时间为43.6 h;倒置显微镜及Giemsa染色镜下观察细胞为多边形及梭形;透射电镜观察可见细胞表面有微绒毛,核高度不规则,核浆比例增大,核仁明显等;染色体数目和结构异常,具有癌细胞特性.广谱细胞角蛋白(PCK)染色阳性,说明其自上皮而非来自基质;同时ZHL2006细胞系在软琼脂中可形成克隆,具裸鼠致瘤性.该乳腺肿瘤细胞系经体外长期培养后已形成永生化细胞系,并具有明显的恶性细胞表型.裸鼠接种实验成功率为6/10,复制肿瘤细胞和原发肿瘤形态结构一致,细胞培养形态结构一致.结论 本研究成功的建立了大鼠乳腺肿瘤细胞系,该细胞系具有典型的恶性肿瘤特征,能应用于裸鼠并成功复制出乳腺肿瘤模型.     

人体乳腺癌细胞超微结构的体视学分析及秩和检验

用体视学方法分别对20例中期和20例晚期女性乳腺浸润性硬性单纯癌的四种癌细胞器的23个形态参数进行体视学的统测和秩和计量分析,筛选出区别度最高的3个最佳参数。研究发现最突出的是统计推断出线粒体的3个最佳参数的癌早期参数值,并绘出了分布曲线。最后探讨了线粒体在不同期形态结构的变异程度与患者预后的关系。     

自发性树鼩乳腺肿瘤的特性(英文)

乳腺癌是严重危害女性健康的常见恶性肿瘤,建立合适的乳腺癌动物模型对于研究人类乳腺癌的生物学机制及发展新的防治方法至关重要。相对于常用的啮齿类动物,树鼩(Tupaia belangeri chinensis,tree shrew)因在进化层次上更接近于人类而可用于建立更适合的乳腺癌模型。该文详细了介绍一例树鼩自发性乳头状良性乳腺肿瘤。免疫组化结果显示该例肿瘤孕激素受体阳性且Ki-67阳性细胞比例显著增加;而活化的Caspase3阳性细胞比例较低;且肿瘤的形态和病理与人导管内乳头状肿瘤非常接近。提示利用树鼩建立乳腺肿瘤模型的可行性。     

乳腺肿块X线检查常见漏、误诊原因的病例分析

目的:分析乳腺X线检查的误漏诊原因,提高诊断准确性。方法:选择2011年3月至2013年12月来我院就诊的135例经乳腺X线摄影和病理检查证实的乳腺肿瘤患者为研究对象,将X线摄影结果与病理检查结果对比,进行回顾性的分析。结果:病理诊断72例良性肿瘤而X线误诊为恶性7例(误诊率9.72%);63例恶性肿瘤而X线误诊为良性5例(漏诊率7.93%)。结论:乳腺X线误诊与乳腺致密程度、患者年龄以及肿瘤形态相关。掌握拍片技术减少技术性误差,提高影像质量,诊断时仔细阅片并熟知各类型乳腺疾病的特征性X影像表象,并与临床相结合,增强责任心,可减少乳腺X线检查的误漏诊。     

利用δ声波场和近红外光实现乳腺肿瘤的精确定位

本文提出一种利用δ声波场和近红外光漫射理论实现球形乳腺肿瘤精确定位的新颖思路。通过构建一个δ波形的声波场,作用到乳腺组织中从而改变组织内某一点的光学特性参数,这种改变对组织表面光分布的影响可以视为微扰,通过控制其作用点在深度上扫描,测得一系列乳腺组织表面一级微扰光分布,从中提取肿瘤与正常组织的差异特性,实现乳腺肿瘤的精确定位和大小测定。该方法具有广泛的临床医学应用前景,为乳腺癌的早期检测提供一种全新思路。     

超声图像处理中Snake模型研究

Snake模型是一种基于高层信息的有效目标轮廓提取算法,其优点是作用过程及最后结果的目标轮廓是一条完整的曲线,从而引起广泛的关注。鉴于医学超声图像的信噪比较低,用经典的边缘提取算法无法得到较好的结果,因此人们将Snake模型进行了各种各样的改进,并且越来越多地将它运用到医学超声图像处理中来。本文对乳腺超声图像进行阈值分割、形态滤波等一系列预处理后,将改进的Snake模型对乳腺超声图像进行肿瘤的边缘提取,得到了比较好的结果。     

基于改进型组织特异纹理滤波器的乳腺肿瘤超声图像处理

基于乳腺超声图像的多参数纹理分类实验,改进了Gjenna Sfippel等的自适应纹理滤波器,通过引入模糊函数、增加重叠区域和迭代次数的措施,在减少图像噪声的同时,增强肿瘤与周围正常组织的视觉差别。量化比较乳腺超声图像经该滤波算法和几种常用滤波算法处理前后的的统计特征参量和肿瘤边缘检测的精确率,验证了该算法的有效性和优越性。     

饮用经臭氧处理的灭菌水与小鼠自发性乳腺肿瘤的发生

目的研究饮用经臭氧处理的灭菌水对小鼠自发性乳腺肿瘤发生的影响。方法给昆明(KM)小鼠、NIH小鼠和BALB/c小鼠饮用经臭氧处理的灭菌水,然后观察其乳腺肿瘤的发生情况,剖检小鼠并从其乳腺、左右肺叶、气管、肺门淋巴结、鼠蹊部淋巴结、心、肝、脾、肾、脑和大小肠等部位取材固定,常规病理制片进行病理组织学检查。结果饮用经臭氧处理的水数月后,KM小鼠、NIH小鼠和BALB/c小鼠,自发性乳腺肿瘤的数量明显增加。发生的乳腺肿瘤均为乳腺腺癌,其中一例为乳头状囊腺癌。乳腺肿瘤均发生在生育3～4胎以上的雌性小鼠。9例中有3例发生肺转移癌。根据乳腺肿瘤发生部位、大体形态特征及病理组织学,结合临床发病情况,即可明确诊断。结论长期饮用经臭氧处理的灭菌水可使小鼠自发性乳腺肿瘤的发生率明显上升。     

冰冻切片与石蜡切片对乳腺肿瘤的诊断价值研究

目的:分析和比较冰冻切片与石蜡切片对乳腺肿瘤的诊断价值。方法:选取480例新鲜乳腺标本,将其制成冰冻切片以及石蜡切片,根据诊断结果进行对比分析,评价乳腺肿瘤的冰冻切片与石蜡切片的对乳腺肿瘤的诊断价值。结果:经石蜡切片诊断乳腺良性肿瘤277例,占57.71%,良性肿瘤中以乳腺纤维瘤诊断居多;经石蜡切片诊断乳腺恶性肿瘤203例,占42.29%,以乳腺浸润性导管癌居多。冰冻切片诊断乳腺良性肿瘤279例,占58.13%;恶性肿瘤195例,占40.62%;延迟诊断6例,占1.25%。以石蜡切片诊断结果为金标准,冰冻切片诊断乳腺良性肿瘤的准确率为98.56%(273/277),诊断恶性肿瘤的准确率为95.07%(193/203),假阳性率为0.72%(2/277),假阴性率为2.96%(6/203),冰冻切片与石蜡切片诊断乳腺肿瘤的结果具有显著一致性,K值为0.965(P0.05)。结论:冰冻切片与石蜡切片诊断乳腺肿瘤的符合率较高,可作为术中快速病理检测的手段,但该种切片方式存在少量延迟诊断,多与术者操作经验有关,故术中应注重制片过程,提高冰冻切片质量。     

Multiple characteristics analysis of Alzheimer’s electroencephalogram by power spectral density and Lempel–Ziv complexity

To investigate the electroencephalograph (EEG) background activity in patients with Alzheimer’s disease (AD), power spectrum density (PSD) and Lempel–Ziv (LZ) complexity analysis are proposed to extract multiple effective features of EEG signals from AD patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared with the control group, the relative PSD of AD group is significantly higher in the theta frequency band while lower in the alpha frequency bands. In order to explore the nonlinear information, Lempel–Ziv complexity (LZC) and multi-scale LZC is further applied to all electrodes for the four frequency bands. Analysis results demonstrate that the group difference is significant in the alpha frequency band by LZC and multi-scale LZC analysis. However, the group difference of multi-scale LZC is much more remarkable, manifesting as more channels undergo notable changes, particularly in electrodes O1 and O2 in the occipital area. Moreover, the multi-scale LZC value provided a better classification between the two groups with an accuracy of 85.7 %. In addition, we combine both features of the relative PSD and multi-scale LZC to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature, reaching 91.4 %. The obtained results show that analysis of PSD and multi-scale LZC can be taken as a potential comprehensive measure to distinguish AD patients from the normal controls, which may benefit our understanding of the disease.     

Feature space resampling for protein conformational search

De novo protein structure prediction requires location of the lowest energy state of the polypeptide chain among a vast set of possible conformations. Powerful approaches include conformational space annealing, in which search progressively focuses on the most promising regions of conformational space, and genetic algorithms, in which features of the best conformations thus far identified are recombined. We describe a new approach that combines the strengths of these two approaches. Protein conformations are projected onto a discrete feature space which includes backbone torsion angles, secondary structure, and beta pairings. For each of these there is one “native” value: the one found in the native structure. We begin with a large number of conformations generated in independent Monte Carlo structure prediction trajectories from Rosetta. Native values for each feature are predicted from the frequencies of feature value occurrences and the energy distribution in conformations containing them. A second round of structure prediction trajectories are then guided by the predicted native feature distributions. We show that native features can be predicted at much higher than background rates, and that using the predicted feature distributions improves structure prediction in a benchmark of 28 proteins. The advantages of our approach are that features from many different input structures can be combined simultaneously without producing atomic clashes or otherwise physically inviable models, and that the features being recombined have a relatively high chance of being correct. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers

PurposeTo address high false-positive results of FFDM issue, we make the first effort to develop a computer-aided diagnosis (CAD) scheme to analyze and distinguish breast lesions.MethodThe breast lesion regions were first segmented and depicted on FFDM images from 106 patients. In this work, 11 gray-level gap-length matrix texture features and 12 shape features were extracted form craniocaudal view and mediolateral oblique view, and then Student’s t-test, Fisher-score and Relief-F were introduced to select features. We also investigated the effect of three factors, i.e., discretisation, selection methods and classifier methods, of the classification performance via analysis of variance. Finally, a classification model was constructed. Spearman’s correlation coefficient analysis was conducted to assess the internal relevance of features.ResultsThe proposed scheme using Student’s t-test achieved an area under the receiver operating characteristic curve (AUC) value of 0.923 at 512 bins. The AUC values are 0.884, 0.867, 0.874 and 0.901 for the low gray-level gaps emphasis (LGGE), solidity, extent, and the combined set, respectively. Solidity and extent depicts the correlation coefficient of 0.86 (P < 0.05).ConclusionsWe present a new CAD scheme based on the contribution of the significant factors. The experimental results demonstrate that the presented scheme can be used to successfully distinguish breast carcinoma lesions and benign fibroadenoma lesions in our FFDM dataset and the MIAS dataset, which may provide a CAD method to assist radiologists in diagnosing and interpreting screening mammograms. Moreover, we found that LGGE, solidity and extent features show great potential for breast lesion classification.     

A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification accuracy of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.     

A self-attention model for inferring cooperativity between regulatory features

Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. We present SATORI, a Self-ATtentiOn based model to detect Regulatory element Interactions. Our approach combines convolutional layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between regulatory elements in a sequence. A comprehensive evaluation demonstrates the ability of SATORI to identify numerous statistically significant TF-TF interactions, many of which have been previously reported. Our method is able to detect higher numbers of experimentally verified TF-TF interactions than existing methods, and has the advantage of not requiring a computationally expensive post-processing step. Finally, SATORI can be used for detection of any type of feature interaction in models that use a similar attention mechanism, and is not limited to the detection of TF-TF interactions.     

DROP: an SVM domain linker predictor trained with optimal features selected by random forest

MOTIVATION: Biologically important proteins are often large, multidomain proteins, which are difficult to characterize by high-throughput experimental methods. Efficient domain/boundary predictions are thus increasingly required in diverse area of proteomics research for computationally dissecting proteins into readily analyzable domains. RESULTS: We constructed a support vector machine (SVM)-based domain linker predictor, DROP (Domain linker pRediction using OPtimal features), which was trained with 25 optimal features. The optimal combination of features was identified from a set of 3000 features using a random forest algorithm complemented with a stepwise feature selection. DROP demonstrated a prediction sensitivity and precision of 41.3 and 49.4%, respectively. These values were over 19.9% higher than those of control SVM predictors trained with non-optimized features, strongly suggesting the efficiency of our feature selection method. In addition, the mean NDO-Score of DROP for predicting novel domains in seven CASP8 FM multidomain proteins was 0.760, which was higher than any of the 12 published CASP8 DP servers. Overall, these results indicate that the SVM prediction of domain linkers can be improved by identifying optimal features that best distinguish linker from non-linker regions.